Falls are a major health concern for older adults, and wearable sensors have been widely explored for detecting falls and enabling timely intervention. However, real-world falls are extremely rare: collecting 100 of them requires an estimated 100,000 days of monitoring, resulting in severely limited labelled data for training machine learning models.
arXiv:2608. 13197v1 Announce Type: new Abstract: Falls are a major health concern for older adults, and wearable sensors have been widely explored for detecting falls and enabling timely intervention.
By Timilehin B. Aderinola, Ilaria D'Ascanio, Luca Palmerini, Lorenzo Chiari, Jochen Klenk, Clemens Becker, Brian Caulfield, Georgiana Ifrim
arXiv:2607. 15400v1 Announce Type: cross Abstract: Falls among older adults are a major safety challenge, but continuous monitoring is difficult to sustain.
By Tasmiah Haque, Jacob Kosinski, Sumit Mohan, Srinjoy Das, Mohammad Abdullah Al-Mamun
arXiv:2606. 18732v1 Announce Type: new Abstract: This work presents the development of hybrid models that integrate spiking neural networks (SNNs) with components of convolutional neural networks (CNNs) to learn from simulated event-based camera data (Dynamic Vision Sensor, DVS) generated from conventional smartphone videos.
By Guillermo Rojas, Gonzalo Soto, Daniel Yunge
arXiv:2607. 09402v1 Announce Type: new Abstract: Deep learning models dependency on large-scale inertial datasets presents a significant bottleneck in inertial sensor-based classification tasks, such as human activity recognition and smartphone location recognition.
By Ofir Kruzel, Itzik Klien
arXiv:2607. 18361v1 Announce Type: cross Abstract: Deep neural networks have become a promising approach for IMU-based sensing, but their scalability is fundamentally limited by costly labeled data and poor robustness to heterogeneous devices, placements, and users.
By Yuyang Leng (Richard), Renyuan Liu (Richard), Shaohan Hu (Richard), Peijun Zhao (Richard), Chun-Fu Chen (Richard), Songqing Chen, Shuochao Yao